Metaheuristic Clustering

Swagatam Das, Ajith Abraham, Amit Konar (auth.)9783540921721, 3540921729

Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention.

In this Volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges.

Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.

Table of contents :
Front Matter….Pages –
Metaheuristic Pattern Clustering – An Overview….Pages 1-62
Differential Evolution Algorithm: Foundations and Perspectives….Pages 63-110
Modeling and Analysis of the Population-Dynamics of Differential Evolution Algorithm….Pages 111-135
Automatic Hard Clustering Using Improved Differential Evolution Algorithm….Pages 137-174
Fuzzy Clustering in the Kernel-Induced Feature Space Using Differential Evolution Algorithm….Pages 175-211
Clustering Using Multi-objective Differential Evolution Algorithms….Pages 213-238
Conclusions and Future Research….Pages 239-247
Back Matter….Pages –